





'''simple docstring'''






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_=0.01		,   a_=1_000						):
			lowerCAmelCase  :     int       						= p_stop
			lowerCAmelCase  :     List[str]       						= max_length





	def __iter__( self						):
			lowerCAmelCase  :     int       						= 0
			lowerCAmelCase  :     Any       						= False
			while not stop and count < self.max_length:
					yield count
					count += 1
					lowerCAmelCase  :     int       						= random.random() < self.p_stop






class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_=False		,   a_=True						):
			lowerCAmelCase  :     Tuple       						= [
			    BatchSamplerShard(a_		,   2		,   a_		,   split_batches=a_		,   even_batches=a_						)
			    for i in range(2						)
			]
			lowerCAmelCase  :     Dict       						= [list(a_						) for batch_sampler_shard in batch_sampler_shards]
			if not split_batches:
					self.assertListEqual([len(a_						) for shard in batch_sampler_shards]		,   [len(a_						) for e in expected]						)
			self.assertListEqual(a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			# Check the shards when the dataset is a round multiple of total batch size.
			lowerCAmelCase  :     Any       						= BatchSampler(range(24						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     str       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			lowerCAmelCase  :     int       						= BatchSampler(range(24						)		,   batch_size=3		,   drop_last=a_						)
			# Expected shouldn't change
			self.check_batch_sampler_shards(a_		,   a_						)

			# Check the shards when the dataset is a round multiple of batch size but not total batch size.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(21						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Dict       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(21						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     List[Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
			# num_processes batch.
			lowerCAmelCase  :     List[str]       						= BatchSampler(range(22						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			lowerCAmelCase  :     Tuple       						= BatchSampler(range(22						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
			# num_processes batch.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(20						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			lowerCAmelCase  :     str       						= BatchSampler(range(20						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     int       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_						)

			# Check the shards when the dataset is very small.
			lowerCAmelCase  :     str       						= BatchSampler(range(2						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[Any]       						= [[[0, 1, 0]], [[1, 0, 1]]]
			self.check_batch_sampler_shards(a_		,   a_						)

			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(2						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     int       						= [[], []]
			self.check_batch_sampler_shards(a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			# Check the shards when the dataset is a round multiple of batch size.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(24						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[int]       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			lowerCAmelCase  :     int       						= BatchSampler(range(24						)		,   batch_size=4		,   drop_last=a_						)
			# Expected shouldn't change
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size.
			lowerCAmelCase  :     int       						= BatchSampler(range(22						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Dict       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			lowerCAmelCase  :     List[Any]       						= BatchSampler(range(22						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     int       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size or num_processes.
			lowerCAmelCase  :     str       						= BatchSampler(range(21						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     List[Any]       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			lowerCAmelCase  :     Dict       						= BatchSampler(range(21						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     str       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			# Check the shards when the dataset is very small.
			lowerCAmelCase  :     Any       						= BatchSampler(range(2						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[int]       						= [[[0, 1]], [[0, 1]]]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)

			lowerCAmelCase  :     List[Any]       						= BatchSampler(range(2						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Dict       						= [[], []]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_						)



	def 	_lowerCamelCase     ( self						):
			# Check the shards when the dataset is a round multiple of total batch size.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(24						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Any       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			lowerCAmelCase  :     Tuple       						= BatchSampler(range(24						)		,   batch_size=3		,   drop_last=a_						)
			# Expected shouldn't change
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			# Check the shards when the dataset is a round multiple of batch size but not total batch size.
			lowerCAmelCase  :     Tuple       						= BatchSampler(range(21						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			lowerCAmelCase  :     int       						= BatchSampler(range(21						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Any       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
			# num_processes batch.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(22						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			lowerCAmelCase  :     Optional[Any]       						= BatchSampler(range(22						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     str       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
			# num_processes batch.
			lowerCAmelCase  :     Optional[Any]       						= BatchSampler(range(20						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[Any]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			lowerCAmelCase  :     Optional[int]       						= BatchSampler(range(20						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     List[str]       						= [
			    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
			    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			# Check the shards when the dataset is very small.
			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(2						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Any       						= [[[0, 1]], []]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)

			lowerCAmelCase  :     Union[str, Any]       						= BatchSampler(range(2						)		,   batch_size=3		,   drop_last=a_						)
			lowerCAmelCase  :     Union[str, Any]       						= [[], []]
			self.check_batch_sampler_shards(a_		,   a_		,   even_batches=a_						)



	def 	_lowerCamelCase     ( self						):
			# Check the shards when the dataset is a round multiple of batch size.
			lowerCAmelCase  :     Optional[Any]       						= BatchSampler(range(24						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Tuple       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			lowerCAmelCase  :     List[str]       						= BatchSampler(range(24						)		,   batch_size=4		,   drop_last=a_						)
			# Expected shouldn't change
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size.
			lowerCAmelCase  :     Dict       						= BatchSampler(range(22						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Optional[int]       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			lowerCAmelCase  :     Any       						= BatchSampler(range(22						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     List[str]       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			# Check the shards when the dataset is not a round multiple of batch size or num_processes.
			lowerCAmelCase  :     Dict       						= BatchSampler(range(21						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Tuple       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			lowerCAmelCase  :     str       						= BatchSampler(range(21						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Tuple       						= [
			    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
			    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
			]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			# Check the shards when the dataset is very small.
			lowerCAmelCase  :     Tuple       						= BatchSampler(range(2						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Tuple       						= [[[0, 1]], []]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)

			lowerCAmelCase  :     int       						= BatchSampler(range(2						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     Tuple       						= [[], []]
			self.check_batch_sampler_shards(a_		,   a_		,   split_batches=a_		,   even_batches=a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
			lowerCAmelCase  :     Optional[Any]       						= [BatchSamplerShard(a_		,   2		,   a_		,   even_batches=a_						) for i in range(2						)]

			self.assertEqual(len(batch_sampler_shards[0]						)		,   3						)
			self.assertEqual(len(batch_sampler_shards[1]						)		,   2						)

			self.assertListEqual(list(batch_sampler_shards[0]						)		,   [[0, 1, 2], [5, 6, 7, 8], [12, 13]]						)
			self.assertListEqual(list(batch_sampler_shards[1]						)		,   [[3, 4], [9, 10, 11]]						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_=False		,   a_=2		,   a_=False						):
			random.seed(a_						)
			lowerCAmelCase  :     Optional[Any]       						= list(a_						)

			lowerCAmelCase  :     Dict       						= [
			    IterableDatasetShard(
			        a_		,   batch_size=a_		,   drop_last=a_		,   num_processes=a_		,   process_index=a_		,   split_batches=a_		,   )
			    for i in range(a_						)
			]
			lowerCAmelCase  :     List[Any]       						= []
			for iterable_dataset_shard in iterable_dataset_shards:
					# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
					random.seed(a_						)
					iterable_dataset_lists.append(list(a_						)						)

			lowerCAmelCase  :     int       						= batch_size // num_processes if split_batches else batch_size
			# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
			lowerCAmelCase  :     Optional[Any]       						= iterable_dataset_lists[0]
			for l in iterable_dataset_lists[1:]:
					self.assertEqual(len(a_						)		,   len(a_						)						)
					self.assertTrue(len(a_						) % shard_batch_size == 0						)

			lowerCAmelCase  :     Tuple       						= []
			for idx in range(0		,   len(a_						)		,   a_						):
					for l in iterable_dataset_lists:
							observed += l[idx : idx + shard_batch_size]

			if not drop_last:
					while len(a_						) < len(a_						):
							reference += reference
			self.assertListEqual(a_		,   reference[: len(a_						)]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= 42
			lowerCAmelCase  :     List[Any]       						= RandomIterableDataset()

			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)

			# Edge case with a very small dataset
			lowerCAmelCase  :     int       						= RandomIterableDataset(max_length=2						)

			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)
			self.check_iterable_dataset_shards(a_		,   a_		,   batch_size=4		,   drop_last=a_		,   split_batches=a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= BatchSampler(range(16						)		,   batch_size=4		,   drop_last=a_						)
			lowerCAmelCase  :     int       						= SkipBatchSampler(a_		,   2						)
			self.assertListEqual(list(a_						)		,   [[8, 9, 10, 11], [12, 13, 14, 15]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= SkipDataLoader(list(range(16						)						)		,   batch_size=4		,   skip_batches=2						)
			self.assertListEqual([t.tolist() for t in dataloader]		,   [[8, 9, 10, 11], [12, 13, 14, 15]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= DataLoader(list(range(16						)						)		,   batch_size=4						)
			lowerCAmelCase  :     Optional[int]       						= skip_first_batches(a_		,   num_batches=2						)
			self.assertListEqual([t.tolist() for t in new_dataloader]		,   [[8, 9, 10, 11], [12, 13, 14, 15]]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= DataLoaderShard(list(range(16						)						)		,   batch_size=4						)
			for idx, _ in enumerate(a_						):
					self.assertEqual(dataloader.end_of_dataloader		,   idx == 3						)

			# Test it also works on the second iteration
			for idx, _ in enumerate(a_						):
					self.assertEqual(dataloader.end_of_dataloader		,   idx == 3						)





	def 	_lowerCamelCase     ( self						):
			Accelerator()
			lowerCAmelCase  :     str       						= DataLoaderDispatcher(range(16						)		,   batch_size=4						)
			for idx, _ in enumerate(a_						):
					self.assertEqual(dataloader.end_of_dataloader		,   idx == 3						)

			# Test it also works on the second iteration
			for idx, _ in enumerate(a_						):
					self.assertEqual(dataloader.end_of_dataloader		,   idx == 3						)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



